Cache stampede prevention in HLD - Architecture Diagram

System Overview - Cache stampede prevention

This system prevents cache stampede, a problem where many users request the same data simultaneously when the cache expires, causing a heavy load on the database. The system uses techniques like request locking and early cache refresh to keep the cache healthy and reduce database overload.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Cache Layer <--> Request Lock Manager
  |               |
  v               v
Database         Background Refresher
  

Components

User

client

Sends requests for data

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Receives requests, checks cache, and manages request locking

Cache Layer

cache

Stores frequently requested data to reduce database load

Request Lock Manager

lock_manager

Prevents multiple requests from querying the database simultaneously for the same data

Database

database

Stores the authoritative data

Background Refresher

service

Proactively refreshes cache before expiration to avoid stampede

Request Flow - 12 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Cache Layer

Cache Layer → API Gateway

API Gateway → Request Lock Manager

Request Lock Manager → API Gateway

API Gateway → Database

Database → API Gateway

API Gateway → Cache Layer

API Gateway → Request Lock Manager

API Gateway → User

Background Refresher → Cache Layer

Failure Scenario

Component Fails:Request Lock Manager

Impact:Multiple requests query the database simultaneously on cache miss, causing high DB load and possible overload.

Mitigation:Implement distributed locking with high availability or fallback to rate limiting to reduce DB pressure.

Architecture Quiz - 3 Questions

What component prevents multiple requests from querying the database at the same time for the same data?

A Request Lock Manager

B Load Balancer

C Background Refresher

D API Gateway

Design Principle

This architecture uses locking and proactive cache refresh to prevent cache stampede, ensuring that only one request queries the database on cache miss and that cache is refreshed early to reduce load spikes.

Practice

1. What is the main goal of cache stampede prevention in system design?

easy

A. To prevent many requests from hitting the database simultaneously

B. To increase the size of the cache storage

C. To reduce the number of cache servers

D. To encrypt cached data for security

Solution

1. Step 1: Understand cache stampede problem

   Cache stampede happens when many requests try to fetch data from the database because the cache expired or missed.

2. Step 2: Identify the goal of prevention

   The goal is to stop all these requests from hitting the database at once to avoid overload.

3. Final Answer:

   To prevent many requests from hitting the database simultaneously -> Option A

4. Quick Check:

   Cache stampede prevention = prevent database overload [OK]

Hint: Focus on reducing database hits during cache misses [OK]

Common Mistakes:

• Confusing cache size with stampede prevention
• Thinking encryption is related to stampede
• Assuming reducing cache servers helps stampede

2. Which of the following is a common technique used to prevent cache stampede?

easy

A. Storing data only in the database

B. Increasing cache TTL to infinite

C. Disabling cache for all requests

D. Using distributed locks around cache refresh

Solution

1. Step 1: Review common stampede prevention methods

   Techniques include locking, early expiration, and request coalescing to control cache refresh.

2. Step 2: Match technique to options

   Distributed locks prevent multiple processes from refreshing cache simultaneously, stopping stampede.

3. Final Answer:

   Using distributed locks around cache refresh -> Option D

4. Quick Check:

   Locking = stampede prevention [OK]

Hint: Look for locking or coordination methods in options [OK]

Common Mistakes:

• Choosing infinite TTL which can cause stale data
• Disabling cache which increases load
• Ignoring locking as a prevention method

3. Consider this pseudo-code for cache stampede prevention:

if cache miss:
  acquire lock
  if cache still miss:
    data = fetch from DB
    update cache
  release lock
return cached data

What happens if the lock is not used here?

medium

A. Multiple requests fetch from DB simultaneously causing overload

B. Cache never updates and returns stale data

C. Requests wait indefinitely causing deadlock

D. Cache size increases exponentially

Solution

1. Step 1: Analyze the role of the lock

   The lock ensures only one request fetches data from DB and updates cache on miss.

2. Step 2: Consequence of missing lock

   Without lock, many requests detect cache miss and all fetch from DB, causing overload.

3. Final Answer:

   Multiple requests fetch from DB simultaneously causing overload -> Option A

4. Quick Check:

   No lock = DB overload [OK]

Hint: Lock prevents simultaneous DB fetch on cache miss [OK]

Common Mistakes:

• Thinking cache never updates without lock
• Confusing deadlock with missing lock
• Assuming cache size grows without lock

4. A system uses early expiration to prevent cache stampede by setting cache TTL shorter than data TTL. However, users report stale data sometimes. What is the likely cause?

medium

A. Cache TTL is longer than data TTL causing stale reads

B. Cache is not refreshed after early expiration due to missing lock

C. Database is down causing stale cache

D. Cache size is too small causing evictions

Solution

1. Step 1: Understand early expiration technique

   Early expiration refreshes cache before data TTL ends to avoid stampede.

2. Step 2: Identify cause of stale data

   If cache refresh is not coordinated (missing lock), cache may not update properly, causing stale data.

3. Final Answer:

   Cache is not refreshed after early expiration due to missing lock -> Option B

4. Quick Check:

   Missing lock = stale cache after early expiration [OK]

Hint: Check if locking coordinates cache refresh [OK]

Common Mistakes:

• Assuming TTL mismatch causes stale data
• Blaming database downtime without evidence
• Confusing cache size with freshness

5. You design a high-traffic API with cache stampede prevention. Which combination best prevents stampede while ensuring fresh data and minimal DB load?

hard

A. Disable cache and use database connection pooling

B. Use long cache TTL with no locking and rely on DB scaling

C. Use short cache TTL with distributed locking and request coalescing

D. Use infinite cache TTL and manual cache invalidation

Solution

1. Step 1: Evaluate each option for stampede prevention

   Use long cache TTL with no locking and rely on DB scaling risks DB overload without locking; C disables cache increasing DB load; D risks stale data.

2. Step 2: Identify best practice combination

   Short TTL triggers refresh often; distributed locking prevents multiple refreshes; request coalescing merges requests to reduce DB hits.

3. Final Answer:

   Use short cache TTL with distributed locking and request coalescing -> Option C

4. Quick Check:

   Locking + coalescing + short TTL = best stampede prevention [OK]

Hint: Combine locking and coalescing with short TTL for best results [OK]

Common Mistakes:

• Ignoring locking and causing DB overload
• Disabling cache increases latency and load
• Using infinite TTL causes stale data